In this hands-on, beginner-friendly workshop, you'll learn how to build a Corrective Retrieval-Augmented Generation (RAG) system using LangGraph, an emerging framework for agentic workflows. We’ll walk through how to design an AI agent that not only retrieves relevant information but also self-corrects and refines its answers through structured reasoning and feedback loops. By the end of the session, participants will: Understand the basics of RAG systems and agentic workflows Explore LangGraph’s intuitive graph-based interface for orchestrating multi-step logic Build a simple corrective agent that improves accuracy over iterations See real-world use cases where agentic RAG boosts reliability and user trust No prior experience with LangGraph or advanced LLMs is required—just a curiosity to learn how next-gen AI agents work behind the scenes.